RNNCell

class brainpy.dyn.RNNCell(num_in, num_out, state_initializer=ZeroInit, Wi_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=[3949182063 2081860855]), Wh_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=[3949182063 2081860855]), b_initializer=ZeroInit, activation='relu', mode=None, train_state=False, name=None)

Basic fully-connected RNN core.

Given \(x_t\) and the previous hidden state \(h_{t-1}\) the core computes

\[h_t = \mathrm{ReLU}(w_i x_t + b_i + w_h h_{t-1} + b_h)\]

The output is equal to the new state, \(h_t\).

Parameters:

• num_in (int) – The dimension of the input vector

• num_out (int) – The number of hidden unit in the node.

• state_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The state initializer.

• Wi_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The input weight initializer.

• Wh_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The hidden weight initializer.

• b_initializer (optional, callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The bias weight initializer.

• activation (str, callable) – The activation function. It can be a string or a callable function. See brainpy.math.activations for more details.

update(x)

The function to specify the updating rule.